Intra-Urban Health Assessed by Remote-Sensing and GIS

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DESCRIPTION (provided by applicant): This project explores the use of remotely sensed imagery and GIS to enhance our understanding of intraurban inequalities in health, using data for the metropolitan area of Accra, Ghana as a study site. The specific aims are as follows: (1) to derive local (neighborhood) measures of health by combining spatially referenced census data, survey data, and vital statistics into a geographic information system (GIS) for metropolitan Accra; (2) to derive local (neighborhood) measures of the built and natural environment through the classification and analysis of data from remotely sensed imagery; (3) to test the hypothesis that health levels in urban places are importantly influenced by the local neighborhood environmental context, including the natural and built environment, the socio-economic composition of the neighborhood's residents, and the location of a neighborhood within the broader urban environment (including its proximity to health clinics and hospitals); (4) to assess the relative contribution of neighborhood environmental context, population composition, and the neighborhood Iocational attributes to health outcomes in metropolitan Accra; (5) to model the interaction among the variables that predict health levels to determine what changes might be introduced into a neighborhood to bring its overall level of health up to a minimally acceptable standard; and (6) to evaluate how well the remotely-sensed data can, on their own as a proxy, model the intra-urban inequalities in health in ways that might lead these data to be used as health monitoring tools. The research involves three major steps: (1) creation of data layers and specification of georeferenced variables to be measured, including data from the 2000 Census, the 1998 and 2003 Demographic and Health Surveys, the 2003 Women's Health in Accra Survey, the 2003 WHO World Health Survey in Ghana, data from the vital statistics for 1999-2001, and a high resolution multispectral satellite image which will be classified according to the Ridd V-I-S model of urban ecology, subsequent to which a set of landscape metrics will be calculated to measure the built and natural environments; (2) statistical analysis to answer the questions posed by our Specific Aims, which will include spatial statistics, regression, and multi-level approaches; and (3) interpretation and dissemination of the results.